SEARCH ALGORITHMS FOR CONCEPTUAL GRAPH DATABASES

International Journal of Database Management Systems ( IJDMS ) Vol.5, No.1, February 2013

DOI: 10.5121/ijdms.2013.5103 35

SEARCH ALGORITHMS FOR CONCEPTUAL GRAPH

DATABASES

Abdurashid Mamadolimov

Mathematical Modelling Laboratory, Malaysian Institute of Microelectronic Systems,

Kuala Lumpur, Malaysia [email protected]

ABSTRACT

We consider a database composed of a set of conceptual graphs. Using conceptual graphs and graph

homomorphism it is possible to build a basic query-answering mechanism based on semantic search.

Graph homomorphism defines a partial order over conceptual graphs. Since graph homomorphism

checking is an NP-Complete problem, the main requirement for database organizing and managing

algorithms is to reduce the number of homomorphism checks. Searching is a basic operation for database

manipulating problems. We consider the problem of searching for an element in a partially ordered set.

The goal is to minimize the number of queries required to find a target element in the worst case. First we

analyse conceptual graph database operations. Then we propose a new algorithm for a subclass of lattices.

Finally, we suggest a parallel search algorithm for a general poset.

KEYWORDS

Conceptual Graph, Graph Homomorphism, Partial Order, Lattice, Search, Database

1. INTRODUCTION

Knowledge representation and reasoning has been recognized as a central issue in Artificial

Intelligence. Knowledge can be represented symbolically in many ways. One of these ways,

called conceptual graph (CG) representation, is graph formalism, that is, knowledge is

represented by labelled graphs, and reasoning mechanism is based on graph homomorphism. All

kinds of knowledge - ontology, facts, rules and constraints - can be represented by conceptual

graphs.

In this paper, we consider a database composed of a set of conceptual graphs, representing some

assertions about a modelled world. Using conceptual graphs and graph homomorphism a basic

query-answering mechanism can be built based on semantic search. A query made to this base is

itself a conceptual graph. An element f of the database is a real answer candidate for query q if

and only if there is a homomorphism from q to f. We note that graph homomorphism checking is

an NP-Complete problem, that is, homomorphism check is an expensive operation. Therefore the

main requirement for conceptual graph database organizing and managing algorithms is to reduce

the number of homomorphism checks.

Graph homomorphism defines a partial order over conceptual graphs. A finite poset (of CGs) can

be considered a database model for a conceptual graph database. Ordering, updating and retrieval

are the main operations of organizing and managing of CG databases. All these operations can be

represented by the more basic operations: Searching and Finding Parents/Children. In this paper

we consider the searching operation only.


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The problem of searching in partially ordered sets has recently received considerable attention.

Motivating this research are practical problems in filesystem synchronization, software testing,

information retrieval, and so on. In practical applications, the elements of partially ordered sets

can be complicated and comparison of elements may be expensive. In the conceptual graph case,

comparison of elements is equivalent to graph homomorphism check. Since graph

homomorphism is an NP-Complete problem, CG comparison takes a lot of time and there is no

hope to reduce this time. Therefore the efficiency of a search algorithm depends directly on the

number of CG comparisons.

The binary search technique is a fundamental method for finding an element in a totally ordered

set. As in the totally ordered case, the goal is to minimize the number of queries required to find a

target element in the worst case. First we analyse conceptual graph database operations. Then we

propose a new algorithm for a subclass of lattices. Finally, we suggest a parallel search algorithm

for a general poset.

2. RELATED WORKS

Conceptual graphs constitute formalism for knowledge representation. Conceptual graphs were

introduced by Sowa [1] as a combination of existential graphs (Peirce, 1909) and semantic

networks (Richens, 1956). The first book on conceptual graphs [2] applied them to a wide range

of topics in artificial intelligence, computer science, and cognitive science. Since 1991,

Conceptual Graphs have been mathematically developed by Chein and Mugnier [3]. One of the

main requirements for knowledge representation formalism is to be logically founded, which

should have two essential properties with respect to deduction in the target logic: soundness and

completeness. For conceptual graphs the equivalent logic is First Order Logic (FOL). The FOL

semantic was defined in [2], and the soundness of conceptual graph homomorphism with respect

to logical deduction was shown. The first proof of the completeness result, based on the

resolution method, is given by Chein and Mugnier [4].

The NP-Completeness of conceptual graph homomorphism checking was proven in [4]. Several

algorithms have been proposed for checking conceptual graph homomorphism [5-9].

The simplest case of a partially ordered set is a totally ordered set. The binary search technique is

an optimal algorithm for finding an element in a totally ordered set [10]. In [11], Mozes, Onak,

and Weimann present a linear-time algorithm that finds the optimal strategy for searching a tree-

like partially ordered set. Levinson [12] and Ellis [13] have proposed several modifications of

breadth/depth first search technique using some simple properties of partially ordered sets.

Dereniowski [14] proves that finding an optimal search strategy for general posets is hard and

gives a polynomial-time approximation algorithm with sublogarithmic approximation ratio. In

[15], a sorting problem in a partially ordered set is studied. Sibarani and others [16] study the

application of tree-like poset format.

3. PRELIMINARIES

3.1. Partially Ordered Set (Poset)

Definition. A partial order is a binary relation " ≤ " over a set which is reflexive,

antisymmetric, and transitive, that is, for all , and in , we have that:

(i) ≤ (reflexivity);

(ii) if ≤ and ≤ then = (antisymmetry);

(iii) if ≤ and ≤ then ≤ (transitivity).


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A set with a partial order " ≤ " is called a partially ordered set or poset (, ≤). For any two

elements and of a poset (, ≤) we use ≥ if ≤ . Also we use < ( > ) if ≤

( ≥ ) and ≠ . Let (, ≤) be a poset. If ⊤ ∈ such that ∀ ∈ , ≤ ⊤, then ⊤ is called the

top element of (the bottom element ⊥ is dually defined); a poset does not necessarily have a

top (bottom) element, but if it has a top (bottom) element then it is unique.

Let (, ≤) be a poset and be any non-empty, finite subset of : ⊆ , || = , where is a

positive integer. We note that (, ≤) is also a poset. Let be an element of : ∈ . The

following definitions are useful for future description.

(, ) = ∈ : > !; #(, ) = # ∈ : # < !; (, ) = $ ∈ (, ): ∀ ∈ (, ), $ ≤ !; %ℎ'(#(, ) = ∈ #(, ): ∀# ∈ #(, ), ≥ #!; )#*(, ) = |(, )|; +,#*(, ) = |%ℎ'(#(, )|. We use (, ), #(, ), (, ), and %ℎ'(#(, ) for an element of

(, ), #(, ), (, ), and %ℎ'(#(, ) respectively.

3.2. Representation of Posets

For representation of a poset we use lists of descendants or lists of ancestors in special form. Let

., … , 0 be elements of poset . We define partial order over integers 1 = 1,2, … , ! in the

following way: for any , 4 ∈ ′, ≤ 4 if and only if 6 ≤ 7. Two lists are associated with an

element 8 ∈ , ' = 1. . : list of descendants D(') and list of ancestors A('). The list of

descendants includes +,#*(', ′) as a first component of the list and first +,#*(', ′)

ancestors are %ℎ'(#(', ′):

D(')=

9+,#*(', ′); %ℎ'(#.(', ′), … , %ℎ'(#:0;<=(8,>1)(', ′); #:0;<=?8,>@AB.(', ′), … C. The list of ancestors is dually defined.

3.3. Lattice Definition. A lattice is a partially ordered set in which any two elements have a unique least

upper bound (also called the join) and a unique greatest lower bound (also called the meet).

Definition. Let ),( ≤L be a finite lattice. An element Lj ∈ is join-irreducible if j has exactly

one child.

4. CONCEPTUAL GRAPHS

4.1. Definitions

A conceptual graph is a labeled bipartite multigraph. One set of nodes is called the set C of

concept nodes, and the other set R is called the set of relation nodes. If a concept is the '-th

argument of a relation then there is an edge between and that is labeled '. Concept and

relation nodes are labeled by two partially ordered sets. These pair of partially ordered sets is

called vocabulary. An edge labeled i between a relation r and a concept c is denoted ),,( cir .

Formal definition of a vocabulary and conceptual graph can be found in [3, see Basic Conceptual

Graph].


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Definition. Let * and ℎ be two CGs defined over the same vocabulary with the node sets %= ∪ E=

and %F ∪ EF respectively. A homomorphism π from * to ℎ is a mapping from gC to hC and

from gR to hR , which preserves edges and may decrease concept and relation labels, that is:

• ,))(,),((,),,( hcirgcir ∈∈∀ ππ

• ).())((, elelRCe ghgg ≤∪∈∀ π

Let * and ℎ be two CGs defined over the same vocabulary. We say * ≥ ℎ if there is a

homomorphism from * to ℎ. It is easy to show that the introduced order is transitive and

reflexive, but it is not antisymmetric.

Definition. Two CGs * and ℎ are said to be hom-equivalent if * ≥ ℎ and ℎ ≥ *, also denoted

* ≡ ℎ.

Let G and H be two hom-equivalence classes of CGs defined over the same vocabulary. We say

G ≽ H if * ≥ ℎ for some * ∈ G and ℎ ∈ H . It is clear that it does not matter how * and ℎ are

chosen from G and H, they can be any representatives of respective hom-equivalence classes. We

note that the introduced order ≽ is a partial order. Let Ω be the set of all hom-equivalence

classes of CGs defined over a given vocabulary. Let J be a non-empty finite subset of Ω.

Without loss of generality, we can consider a set of representatives of elements of ∆. We call it

a CG dataset. We note that a CG dataset is also a partially ordered set. Let K be any element of

Ω, K ∈ Ω, and let q be any representative of Q, L ∈ K. We call q a query element. A query

comparison is a comparison between query element and an element of a dataset. Only query

comparison needs to check graph homomorphism, but comparison of two elements of a dataset

can be done without graph homomorphism.

4.2. Organizing and Managing Operations

There are three main operations: Ordering, Updating and Retrieval. We note that since a CG

database is a knowledgebase, the inference operation can be also considered. However we limit

ourselves to the first three operations. It is clear that Ordering (generating initial dataset) is

consecutively Inserting of query CG into dataset. Updating can be done using Inserting and

Deleting of query CG. Retrieval uses only Finding Descendants of query CG. Next

representations look like:

• Deleting = Searching + something;

• Inserting = Finding Parents + Finding Children + something;

• Finding Descendants = Finding Children + something.

It can be easily checked that the “something” parts of the above representations do not depend on

query comparisons and take insignificant time. Finding Parents and Finding Children are dual

operations.

Therefore only two operations - Searching and Finding Parents/Children - are enough for

manipulating a CG database. We consider only the Searching operation in a poset. The formal

definition of the Searching problem can be seen in the input-output part of Algorithm 2.

5. SEARCHING IN MATRYOSHKA-LATTICES

There are different classes of posets. We can order poset classes according to how easy they are

to search:


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1. Totally ordered sets (chains);

2. Tree-like posets;

3. Lattices;

4. General posets.

In Figure 1 you can see examples of each class of posets. It is clear that for the searching

Figure 1. Different classes of posets

operation the most amenable class is chains. Existing works show that the next best class is tree-

like posets, then lattices, and the worst class is general posets. In the conceptual graph case we

usually have lattices or general posets.

Next we consider lattices. Let ),( ≤L be a finite lattice. For each element Lx ∈ we define

jxJ )( = join-irreducible: .xj ≤

Theorem [3]. Let ),( ≤L be a finite lattice. For all Lyx ∈, , yx ≤ if and only if ).()( yJxJ ⊆

Let ),( ≤L be a finite lattice. We number join-irreducible elements of ),( ≤L by 1,2,3, …,|MN|,

where MN is the set of join-irreducible elements of N. For each element x of the lattice we can

assign binary code of length |MN|: if the i-th join-irreducible element is less than or equal to x

then the i-th component of the binary code is 1, otherwise it is 0. Regarding the last theorem, it is

clear that for all Lyx ∈, , yx ≤ if and only if the binary code of x is less than or equal to the

binary code of y. In Figure 3, lattice N = NO has six join-irreducible elements. We assigned a

binary code of length 6 to each element of the lattice.

Habib and Nourine [17] have used binary codes for lattice operations. In the searching problem, if

query element has already been compared with join-irreducible elements then it can be compared

with other elements using binary code comparisons. For example, a Boolean lattice with P = 20

elements has logT P = join-irreducible elements and this is enough query comparisons; all

other comparisons can be done using binary code comparison.

Let N be a finite lattice. Let MN be the set of join-irreducible elements of N. We consider the set

N.=MN ∪ ⊤, ⊥. The partially ordered set N. is called the poset generated by join-irreducible

elements of N. We note that N. is not necessarily a lattice. In Figure 2 you can see a

counterexample. If N. is a lattice then we can construct a poset NT generated by the join-

irreducible elements of N.. We can have a sequence N = NO, N., NT, … , N8U., N8, … of lattices,

where N8 is a poset generated by the join-irreducible elements of lattice N8U.. We assumed that for

all ' = 1,2, …, posets N8 are lattices. If a lattice certifies the just explained property then we call it

a matryoshka-lattice. The lattice N = NO in Figure 3 is a matryoshka-lattice, but the (left) lattice in

Figure 2 is not. It is clear that |N8U.| ≥ |N8|. There exists a positive integer such that all elements

of the lattice NV, except the bottom and may be the top elements, are join-irreducible. Let be a

Tree-like poset Lattice General poset Chain


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first such integer. Then any lattice NVBW is the same as lattice NV. Now we have a finite sequence

of mutually different lattices N = NO, N., NT, … , NV. It is clear that NV/⊤ is a tree-like poset and it

has fewer elements than the original lattice L. It is enough to know comparisons between a query

element and elements of NV/⊤. Therefore we have a more simple search space than in the

original one. Using binary codes we can compare a query element with elements of NVU., then

with elements of NVUT, and so on, finally with elements of N. This is the idea of Algorithm 1.

Figure 2. Non-matryoshka-lattice N (left) and poset generated by N (right)

Algorithm 1 first constructs the sequence N = NO, N., NT, … , NV. Then using an efficient search

algorithm for a tree, for example the algorithm from [11], a query element is compared with

elements of NV/⊤. Using binary codes a query element is then compared with elements of the

previous lattice, and so on. Finally Algorithm 1 finds comparisons between query element and

elements of the original lattice N. Algorithm 1 is based on replacement of comparisons of two

elements of the lattice with binary code comparisons. We note that comparison of binary codes is

insignificant relative to comparison of CGs using graph homomorphism

Figure 3. Matryoshka-lattice N (left) and all lattices generated from N

Algorithm 1: SearchInMat-Lattice(Matryoshka-lattice, query element);

Input: Matryoshka-lattice L and query element q;

Output: An element of matryoshka-lattice L, which is equal to query element q or “No q in a

given matryoshka-lattice L”;


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1. ' = 0; 2. N8 = N;

3. If N8 has a non-join-irreducible element (≠ ⊤, ⊥) then

3.1 Find and numerate all join-irreducible elements of N8;

3.2 Assign binary code to each element of N8;

3.3 Construct next lattice N8B. generated by the join-irreducible elements of N8;

3.4 ' = ' + 1; Go to 3;

4. Using search algorithm in a tree N8/⊤ find binary code of query element q;

5. If ' > 0 then ' = ' − 1 else 7;

6. Extend binary code of query element q using binary code comparisons in N8; Go to 5;

7. Search for query element q using binary code comparisons in L.

In Figure 3, lattice N. is generated by the join-irreducible elements of NO, and lattice NT is

generated by the join-irreducible elements of N.. NT/⊤ is a tree-like poset. The bold nodes of

NO, N., and NT are join-irreducible elements. A binary code is assigned to each node of NO and N..

For any given query element, Algorithm 1 uses only three query comparisons. These are

comparisons between a query element and three join-irreducible elements of a tree-like poset

NT/⊤.

6. PARALLEL SEARCH ALGORITHM

Now we consider a general poset. First we propose a simple sequential search algorithm in a

poset, and then using it we suggest a parallel algorithm.

6.1. Sequential Search Algorithm

Let , ≤ be a poset and be any non-empty, finite subset of : ⊆ , || = , where is a

positive integer. We call the set as a dataset. We assume that the dataset has both top and

bottom elements and that they are different: ⊤ ≠⊥. Assume ., T, … , 0 are elements of and

. = ⊤ and 0 =⊥. Let L be an element of : L ∈ . We call the element L a query element.

Our search algorithm is based on the following very simple property of posets: Let , 4, \ be

elements of some poset. If ≥ 4 is FALSE then for any \ such that \ ≤ , we have that \ ≥ 4 is

also FALSE.

Algorithm 2 starts from the top element and moves to depth or width for further searching.

Algorithm 2 asks a question of the form “(# (] ≥ L,4 (]?”. If the answer

is “Yes” then it continues the search in the set of descendants of the selected element (moving to

depth). If the answer is “No” then it searches in the complimentary part (moving to width).

Algorithm 2: SearchInPoset(Dataset, query element);

Input: Elements 8, ' = 1. . of dataset ,

Lists of descendants D(') of elements 8, ' = 1. . of dataset , Query element L ≠⊥);

Output: If there exists an integer such that 1 ≤ ≤ and L = 6 then else “No L in ”;

1. ← 1; //algorithm starts from top element

2. Status() ← YES; //if current element x is more or equal than query element then its

status is YES

3. ` ← 1; //starting from first child of current element

4. 4 ← %ℎ'(#a, ′); //next child of current element is assigned to y

5. If Status4 =NO then go to 7; //status of child may already be NO


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6. If 7 ≥ L then ← 4 and go to 2 else go to 8; //if child more or equal than query then

child is current element and go …

7. If ` ≠ +,#*, ′ then ` ← ` + 1 and go to 4 else go to 9; //if y is not the last child

of the current element then with next child go …

8. If ` ≠ +,#*, ′ then

Status4 ← NO and

for all Descendant4, ′ do Status(Descendant(4, ′))←NO and

` ← ` + 1 and go to 4; // because child is not more or equal than query (step 6) status of child

and all its descendants is NO and with next child go …

9. If 6 ≤ L then go to 11; //if current element is less or equal than query then go …

10. Return “No L in ” and stop;

11. Return ;

We note that the %ℎ'(#a, ′) function returns the `-th child of in ′. Because ∈ ′, the

function can be done in constant time since it corresponds to an access to an element of the

descendant list D. The Descendant4, ′ function is a similar. Also we note that the dual

algorithm of Algorithm 2 can be easily obtained. It can be used if it is known that the query

element is closer to the bottom element.

6.2. Parallel Search Algorithm

For parallel computing we use the parallel random-access machine (PRAM) model of

computation with ., T , … , g, ] ≥ 2 processors. In the PRAM model of computation all

processors can access common a RAM in a single algorithm-step.

In parallel Algorithm 3, the first processor uses Algorithm 2; all other processors use Algorithm 2

with random chosen initial element (step 1.3). Shared information for processors is the Status of

an element, which can be ANC (ancestor), NONANC (non-ancestor), and DES (descendant). In

Algorithm 3 any query comparison (in steps 1.4, 1.10, and 1.14) will be done once only, that is,

two or more processors do not check the same query comparison if they do not come to it at

exactly the same time. In other words, there is no overlapping of partition of search space up to

query comparisons. The whole algorithm stops when one of the processors h returns h or the

first processor . returns “No L in ”. If the processor h, ≠ 1 reaches the bottom element, but

cannot find a searching element then it chooses a new initial element and searches again. This

provides uniform distribution of tasks among the processors.

Algorithm 3: ParallelSearchInPoset(Dataset, query element);

Input: Elements 8, ' = 1. . of dataset ,

Lists of descendants D(') of elements 8, ' = 1. . of dataset , Query element L ≠⊥);

Output: If there exists an integer h such that 1 ≤ h ≤ and L = 6i then h

else “No L in ”;

1. For all processors h, = 1. . ] do in parallel

1.1. If = 1 then h ← 1 and go to 1.5; 1.2. E ← 2,3, … , − 1; 1.3. h ← #E and if Status(h)=ANC then

E ← #h, 1 and go to 1.3

elseif Status(h)=NONANC then

E ← E/h ∪ #h, 1 and go to 1.3;

1.4. If ¬6i≥ L then do

Status(h)←NONANC and

for all Descendant(h, ′) do Status(Descendant(h , ′))←NONANC and


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E ← E/h ∪ #h, 1 and go to 1.3;

1.5. Status(h)←ANC;

1.6. `h ← 1;

1.7. 4h ← %ℎ'(#aih, ′);

1.8. If ≠ 1 and Status(4h)=ANC then E ← #4h , 1 and

go to 1.3;

1.9. If Status4h =NONANC then go to 1.11;

1.10. If 7i≥ L then h ← 4h and go to 1.5 else go to 1.12;

1.11. If `h ≠ +,#*h, ′ then `h ← `h + 1 and go to 1.7 else go to 1.13;

1.12. If `h ≠ +,#*h, ′ then

Status4h ← NONANC and

for all Descendant4h , ′ do Status(Descendant(4h, ′))←NONANC and

`h ← `h + 1 and go to 1.7;

1.13. If Status(h)=NONDES then go to 1.16;

1.14. If 6i≤ L then return h and go to 2;

1.15. Status(h)←NONDES;

1.16. If = 1 then return “No L in ” and go to 2 else go to 1.3;

2. End.

7. CONCLUSIONS

We analyzed conceptual graph database organizing and managing operations. We proposed a

search algorithm for a particular case, when the search space is a special subclass of lattices, so

called matryoshka-lattices. In the general case we suggested a parallel search algorithm which is

based on simple sequential algorithm.

Since the searching in lattices problem is replaced with searching in a tree-like poset with fewer

elements than original one we believe that Algorithm 1 is very efficient. However, we do not

know how large/small the class of matryoshka-lattices is. There is no easy way to check whether

a given lattice is a matryoshka-lattice or not.

We remark that the efficiency of Algorithm 3 can be increased by replacing the basic sequential

algorithm (Algorithm 2) with a faster one.

ACKNOWLEDGEMENTS

This work is supported by Ministry of Science, Technology and Innovation of Malaysia under the

e-Science Fund with project code 06-03-04-SF0032.

I would like to thank Aziza Mamadolimova, Chien Su Fong and Abdumalik Rakhimov for their

valuable comments and suggestions.

REFERENCES

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Algorithms, Philadelphia, PA, USA, Society for Industrial and Applied Mathematics, pp392

[16] Sibarani E.M., Wagenaars P., Bakema G.,

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International Workshop ORDAL'94, number 831 in Lecture Notes in Computer Science, pp1

Springer-Verlag, Lyon, France.

Author Abdurashid MAMADOLIMOV graduated

from the Tashkent State University in 1995. In

2005, he received a Ph.D. degree from the

National University of Uzbekistan for his

research on Applied Mathematics. His

research interests are in the areas of

Mathematics, Combinatorics, Graph Theory,

and Cryptography. Dr. Mamadolimov was the

recipient of the GSFS Scholarship from the

Seoul National University in 2007.


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projection a la derivation sous contraintes. PhD Thesis, University Montpellier II.

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Efficient Projection Algorithms”, In Proc. of ICCS'03, vol. 2746 of LNAI. Springer.

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Wesley, Reading, Massachusetts, second edition.

s S., Onak K., Weimann O., (2008) “Finding an optimal tree searching strategy in linear time”,

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Daskalakis, C., Karp R.M., Mossel E., Riesenfeld S., Verbin E., (2009) “Sorting and selection in

posets”, In: SODA'09: Proceedings of the Nineteenth Annual ACM-SIAM Simposium on Discrete

Algorithms, Philadelphia, PA, USA, Society for Industrial and Applied Mathematics, pp392

Sibarani E.M., Wagenaars P., Bakema G., (2012) “A typical case of recommending the use of a


Habib M. and Nourine L., (1994) “Bit-vector encoding for partially ordered sets”, In P

International Workshop ORDAL'94, number 831 in Lecture Notes in Computer Science, pp1

Verlag, Lyon, France.

Abdurashid MAMADOLIMOV graduated

from the Tashkent State University in 1995. In

2005, he received a Ph.D. degree from the

National University of Uzbekistan for his

research on Applied Mathematics. His

research interests are in the areas of Discrete

Mathematics, Combinatorics, Graph Theory,

and Cryptography. Dr. Mamadolimov was the

recipient of the GSFS Scholarship from the

Seoul National University in 2007.


44

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SEARCH ALGORITHMS FOR CONCEPTUAL GRAPH DATABASES

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